Name
Abstract | ||
|---|---|---|
Radar coincidence imaging (RCI) is a high-resolution and instantaneous imaging technique without the limitation of relative motion between target and radar. In sparse-based RCI, the assumption that the scatterers are located at the pre-discretized grid-cell centers is commonly used. However, the generally existent off-grid degrades the imaging performance considerably. In this paper, the algorithm based on block sparse Bayesian learning (BSBL) framework is developed to solve the off-grid RCI in the range-azimuth space. Applying the Taylor expansion, the unknown true dictionary is approximated to a linear model. Then target reconstruction is reformulated as a block sparse recovery problem. BSBL is then applied to solve the problem by assigning appropriate priors to the coefficients and exploiting the block structure and intra-block correlation. Results of numerical experiments demonstrate that the algorithm can yield superior imaging performance, compared with other block sparse recovery algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SiPS.2015.7344991 | 2015 IEEE Workshop on Signal Processing Systems (SiPS) |
Keywords | Field | DocType |
Radar coincidence imaging (RCI),off-grid,sparse recovery,sparse Bayesian learning (SBL),block sparse | Radar,Bayesian inference,Pattern recognition,Linear model,Computer science,Sparse approximation,Coincidence,Artificial intelligence,Prior probability,Grid,Taylor series | Conference |
Citations | PageRank | References |
1 | 0.39 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoli Zhou | 1 | 31 | 3.95 |
| Hongqiang Wang | 2 | 69 | 9.96 |
| Yongqiang Cheng | 3 | 133 | 29.99 |
| Yuliang Qin | 4 | 142 | 27.06 |
| Xianwu Xu | 5 | 1 | 0.39 |